Local Personal Daemon

ABSTRACT

Systems and methods of a personal daemon, executing as a background process on a mobile computing device, for providing personal assistant to an associated user is presented. While the personal daemon maintains personal information corresponding to the associated user, the personal daemon is configured to not share the personal information of the associated user with any other entity other than the associated user except under conditions of rules established by the associated user. The personal daemon monitors and analyzes the actions of the associated user to determine additional personal information of the associated user. Additionally, upon receiving one or more notices of events from a plurality of sensors associated with the mobile computing device, the personal daemon executes a personal assistance action on behalf of the associated user.

BACKGROUND

More and more people are expressing and demonstrating their interest inhaving computers understand them and provide personalized assistancetailored to their specific needs and context. Of course, to providepersonalized assistance that is tailored to the specific needs andcontext of a person, the assisting process must be aware of many aspectsof the person, i.e., his or her personal information. Indeed, the moreaspects of the person a process knows, the better that process is inpersonalizing information for the person. A key question is, then: howto secure personal information and provide personalized assistance.

SUMMARY

The following Summary is provided to introduce a selection of conceptsin a simplified form that are further described below in the DetailedDescription. The Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

According to aspects of the disclosed subject matter, a mobile computingdevice hosting a personal daemon configured to provide personalassistant to an associated user is presented. The personal daemonexecutes as a background process on the mobile computing device. Whilethe personal daemon maintains personal information corresponding to theassociated user, the personal daemon is configured to not share thepersonal information of the associated user with any other entity otherthan the associated user except under conditions of rules established bythe associated user. The personal daemon monitors and analyzes theactions of the associated user to determine additional personalinformation of the associated user. Additionally, upon receiving one ormore notices of events from a plurality of sensors associated with themobile computing device, the personal daemon executes a personalassistance action on behalf of the associated user.

According to additional aspects of the disclosed subject matter, acomputing device-implemented method for providing personal assistance toa user is presented. In at least one embodiment, the computingdevice-implemented method is implemented as a personal daemon processrunning in the background on the mobile computing device. The methodcomprises receiving a notice of a subscribed event relating to the user.Upon receiving the notice, a set of personal assistance rules in a storeof personal information of the user is consulted to identify one or moreactions to be taken on behalf of the user in regard to receiving thesubscribed event. The identified actions executing, without user input,on the computing device on behalf of the user.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing aspects and many of the attendant advantages of thedisclosed subject matter will become more readily appreciated as theyare better understood by reference to the following description whentaken in conjunction with the following drawings, wherein:

FIG. 1 shows an exemplary graph illustrating the relationship ofpersonal information security as a function of increased personalization(with the commensurate increased amount of access to personalinformation) as is common to third-party, monolithic systems thatprovide personal assistance/personalization to multiple subscribers;

FIG. 2 is a diagram illustrating an exemplary network environment inwhich a computing device, suitably configured with a personal daemon,may operate;

FIG. 3 is a diagram illustrating an exemplary network environmentincluding multiple computing devices associated with the same user;

FIG. 4 is a block diagram illustrating an exemplary computing devicesuitably configured to provide personal assistance by a personal daemon;

FIG. 5 is a block diagram illustrating exemplary processing stages of apersonal daemon according to aspects of the disclosed subject matter;

FIG. 6 is a flow diagram illustrating for providing personal assistanceby a personal daemon; and

FIG. 7 is a flow diagram illustrating an exemplary routine forconducting analysis of user activity to learn and adapt to additionalpersonal information of the associated user.

DETAILED DESCRIPTION

For purposes of clarity, the term “exemplary” in this document should beinterpreted as serving as an illustration or example of something, andit should not be interpreted as an ideal and/or a leading illustrationof that thing. The term “personal information” corresponds toinformation, data, metadata, preferences, behaviors, of the associateduser, as well as rules for interacting with the user. Generallyspeaking, personal information is information about the associated userthat represents some aspect of the user. The personal information maycomprise data such as (by way of illustration and not limitation)gender, age, education, demographic data, residency, citizenship, andthe like. Personal information may also comprise preferences andinterests, expertise, abilities, and the like. Still further, personalinformation may comprise rules (including rules established by theassociated user as well as rules that are learned and/or inferredthrough analysis as described below) for interacting with the associateduser in providing personal assistance.

One solution in providing personalized assistance could be to deploy anonline service that can provide personalized assistance to a largenumber of subscribers by deploying a large numbers of computers and/orprocessors that gather, store, collate, analyze and manipulate largeamounts of data gathered from all over the world. In this monolithicmodel, subscribers wishing to receive personalized assistance and/orrecommendations provide various items of personal information to theonline service and, typically, further permit the online service tomonitor numerous aspects of the subscribers' lives to learn additionalpersonal information about them. Nearly every activity a subscribersmight take (especially with regard to their computer) is captured andanalyzed to identify addition personal information, these activitiesincluding but not limited to online behaviors, purchases, preferences,affiliations, banking information, etc. The online service then deploysvarious processes to provide personalized assistance, based on theamassed personal information that it gathers and maintains of itssubscribers.

Of course, running a massive monolithic online service as describedabove is expensive. In order to keep such a large online serviceoperational, the online service must have a revenue stream. Generallyspeaking, however, subscribers/individuals want the personalizedassistance for free. Rather than directly charging the subscribers a feefor the personalization service, the online service turns to monetizingthe personal information of its subscribers. A common phrase for thismonetization is “ad-funded” or “vendor-funded.” The online servicemonetizes the personal information of its subscribers by identifyingindividuals among its subscribers having various traits, interests,demographics, and attributes (as determined by the personal informationthat the online service has received and learned of its subscribers) andmonetizing the identified information by placing advertisements to thoseindividuals on behalf of advertisers. Of course, selling advertisementsdirected to its subscribers is only one way in which the monolithiconline service (as described above) can monetize the personalinformation of its subscribers. Alternatively, the online service maysimply sell contact lists and/or information.

Subscribers are often delighted to receive personalized assistance, somuch so that they tolerate the advertisements that are frequentlypresented to them. Moreover, they are largely unaware of and would beextremely uncomfortable with how much of their personal information theonline services possesses and monetizes/exposes to third parties (e.g.,advertisers, vendors, organizations, etc.) Of course, an online servicemight placate its subscribers by telling stating that it will do no harmto its subscribers, yet the online service is conflicted: the onlineservice generates revenue by providing personal information of itssubscribers to third parties (whether by advertisements, selling contactlists, etc.). Moreover, the more specific the personal information thatis provided to third parties, the greater the monetary reward is for theonline service. Unfortunately, the more specific personal informationthat is exposed, the greater the risk and the more potential for abuseto the person or persons of the exposed personal information.

Of course, even without considering the risk of exposing personalinformation to known third parties, by simply storing substantialpersonal information for a large number of subscribers an online servicecreates an inviting, enticing target for identity thieves. So, while thelevel of personalized assistance can be directly correlated to theamount of personal information that is known of a person, the personalsecurity of that person (as posed by the risk of exposure or misuse thepersonal information) is also a function of the amount of personalinformation of the person that the online service possesses. As shown inFIG. 1, while the ideal would be high personal security (i.e., securityin personal information) and high personalization, in reality with amonolithic online service the level of personal security (with regard toone's personal information) decreases the as level of personalizationincreases.

In contrast to a monolithic online service and according to aspects ofthe disclosed subject matter, a personal daemon operating on a person'sown computing device is presented. By way of definition, a “daemon” is aprocess or thread of execution, run on a computing device that isexecuted in the background of the computing device rather than beingexecuted under the direct control of a computer user. However, while adaemon executes in the background of the computing device, a computeruser can interact with a daemon and, through the interaction, direct theactivities of the daemon. A “personal daemon” is a daemon that hasaccess to, acquires, infers, maintains, and acts upon personalinformation of a computer user in providing personalized assistance. Apersonal daemon monitors numerous aspects of an associated user'sactivities to identify, infer, and/or learn additional personalinformation (when and where available) regarding the user as well asinferring and learning rules for acting on the user's behalf, i.e.,providing personalized assistance to the user. Additionally, a personaldaemon may learn and/or confirm personal information, particularly inregard to inferred information and/or rules for acting on the user'sbehalf, regarding the user through dialog and other interaction with theuser, including confirming previously derived inferences regarding theuser, requesting user preferences and other personal information, andthe like.

The phrase “personal assistance,” in the context of a personal daemonproviding personal assistance to the associated user, should beinterpreted as carrying out one or more actions on behalf of the userbased. Typically, though not exclusively, the personal assistance istriggered by one or more events related to aspects of the user's currentcontext. By way of example and not limitation, the one or more actionsof personal assistance may include: providing a recommendation to theuser that the user take a particular action; obtaining data and/orservices on the user's behalf; confirming with the user the inference ofpersonal information from analysis of the user's activities; confirmingwith the user authorization for the personal daemon take an action onbehalf of the user; providing a notification to the user regarding oneor more events; providing alternatives to current user activities;recommending a venue; executing an action on behalf of the user on thecomputing device; recommending alternative and/or related activities oritems; and the like. As will be discussed in greater detail below, apersonal daemon provides personal assistance to the user based on rules,personal information of the user, and/or the current context of theuser.

Unlike monolithic online service option that gathers and monetizespersonal information of its subscribers, according to aspects of thedisclosed subject matter a personal daemon does not share the associateduser's personal information with other, third-party entities, except forand according to explicit direction by the user. A third-party entitycorresponds to any entity not owned and/or responsive only to theassociated user.

According various embodiments of the disclosed subject matter, thepersonal daemon operates on the user's computing device solely for thebenefit of the user. Advantageously, the personal daemon is notconflicted by the need to monetize the user's personal information tosupport its operation or other purposes of an external, third-partyentity. Accordingly, the personal daemon enjoys a position of intimatetrust by the user and can be viewed as a computer-based extension of theuser. Indeed, in a real sense the associated user may refer to therelationship as a “we” relationship, i.e., me and my own personaldaemon. As a consequence of this high level of trust, the user is moreinclined to provide the personal daemon with a greater degree of accessto all information related to the associated user and his/her use of amobile device, including personal and/or confidential information. Forexample (for illustration and not limitation), because the personaldaemon does not share personal information of the associated user withothers, the user may be willing to permit the personal daemon toread/scan the emails of the user, have access to and monitor the user'sinteractions on a social network, track the user's online purchasehistory, maintain the user's passwords, analyze all files and datastreams on the mobile device, and the like. By instilling this higherlevel of trust in the associated user, and obtaining access to a greaterdegree of personal information, a personal daemon enhances the level ofpersonalized assistance that can be provided to the user. As will be setforth in greater detail below, based on the enhanced level of access topersonal information, through an enrichment cycle of inferring theassociated user's preferences and choices, and learning rules ofbehavior in a given circumstance, and also validating those inferences,the personal daemon becomes an extension of the associated user,reflecting the associated user's personality and providing complimentarypersonal assistance. Indeed, over time the personal daemon “grows,”becomes more familiar, understands and knows more detail regarding theassociated user, and is better able to provide personal assistance.

Turning to FIG. 2, FIG. 2 is a block diagram illustrating an exemplarynetwork environment 200 in which a computing device, suitably configuredaccording to aspects of the disclosed subject matter with a personaldaemon, may operate. More particularly, the network environment 200includes a user's computing device 202 suitably configure to host apersonal daemon 204. The personal daemon 204 executes on the computingdevice 202 on behalf of the person/user 201 to provide personalassistance to the user. As will be readily appreciated, suitablecomputing devices that may be configured with a personal daemon 204include, by way of illustration and not limitation: tablet computingdevices, such as tablet computing device 202; smart phone devices (notshown); the so called “phablet” computing devices (i.e., computingdevices that straddle the functionality of typical tablet computingdevices and smart phone devices); laptop computers; desktop computers;wearable computing devices; personal digital assistants, and the like.

The network environment 200 also includes a network 210 by which theuser's computing device 202 (by way of components, applications, apps,etc.) can communicate with and access network accessible devices and/oronline services connected to the network, including (by way ofillustration and not limitation): one or more other user computingdevices, such as computing device 212 associated with user 211; socialnetworking sites, such as social networking site 218; online networkservices, such as a search engine 216; shopping and/or commerce sites,such as shopping site 214, and the like.

According to aspects of the disclosed subject matter, a personal daemon204 is configured to operate on the “edge of the cloud,” meaning thatthe personal daemon operates on the user's computing device 202, with orwithout connectivity to the network 210. When connectivity to thenetwork 210 is available (via the connection of the computing device 202to the network), the personal daemon 204 executing on the computingdevice can access data and services for use in providing personalassistance to the user 201.

It is readily appreciated that many users have more than one computingdevice. Indeed, it is common for a user to have, by way of illustration,a smart phone, a tablet computing device, a laptop computer, and/or adesktop computer. Thus, according to aspects of the disclosed subjectmatter, a personal daemon operating on a computing device, such ascomputing device 204, may be configured to share personal informationregarding the associated computer user 201 with a “sibling” personaldaemon, i.e., a personal daemon associated with the same user that isoperating on another computing device. In other words, as a personaldaemon is an extension of one's own self, the personal informationmaintained by one embodiment of a personal daemon on a first computingdevices can share the same and all personal information with anotherembodiment of the personal daemon (a sibling personal daemon) on anotherdevice. Moreover, as discussed below, sibling personal daemons may beconfigured to cooperate in order to provide personal assistance to theassociated user.

FIG. 3 is a diagram illustrating an exemplary network environment 300including multiple computing devices 302 and 306 associated with thesame user 301. As can be seen, each computing device 302 and 306 isconfigured with a personal daemon 304 _(A) and 304 _(B). These personaldaemons, 304 _(A) and 304 _(B), are sibling personal daemons as they areassociated with the same user 301. As sibling personal daemons, they may(according to user 301 authorization) share personal information of theassociated user with each other, share cached data, share and/ordistribute user behavior analysis to identify personal information, andthe like. Sharing of the data, information, and activities may includesharing in a distributed manner, i.e., hosting some of the data oncomputing device with a first sibling personal daemon, offloadingprocessing of monitored user events to the sibling personal daemonhaving the best capacity to conduct that corresponding analyses, and thelike. Inter-communication between sibling personal daemons may occur ondemand (i.e., a just-in-time manner), on scheduled intervals, onexplicit instruction from the user, and the like. Of course, whileconsiderations such as processing capacity, bandwidth, power levels,data access, and the like may be a factor with regard to distributingtasks among sibling personal daemons, these same considerations may beused in determining when a sole personal daemon performs analysis ofuser activity, generates inferences regarding personal information ofthe user, determines rules for responding to various events, and thelink. Indeed, the personal daemon 204 may be configured (or mayself-configure) to have minimal impact on the user's computing device.

Turning now to FIG. 4, FIG. 4 is a block diagram illustrating anexemplary computing device 400 suitably configured to provide personalassistance by a personal daemon. The exemplary computing device 400includes a processor 402 (or processing unit) and a memory 404interconnected by way of a system bus 410. As will be readilyappreciated, the memory 404 typically (but not always) comprises bothvolatile memory 406 and non-volatile memory 408. Volatile memory 406retains or stores information so long as the memory is supplied withpower. In contrast, non-volatile memory 408 is capable of storing (orpersisting) information even when a power supply is not available.Generally speaking, RAM and CPU cache memory are examples of volatilememory 406 whereas ROM, solid-state memory devices, memory storagedevices, and/or memory cards are examples of non-volatile memory 408.

The processor 402 executes instructions retrieved from the memory 404 incarrying out various functions, particularly in regard to executing apersonal daemon 204 that provides personal assistance to the associateduser. The processor 402 may be comprised of any of various commerciallyavailable processors such as single-processor, multi-processor,single-core units, and multi-core units. Moreover, those skilled in theart will appreciate that the novel aspects of the disclosed subjectmatter may be practiced with other computer system configurations,including but not limited to: personal digital assistants, wearablecomputing devices, smart phone devices, tablet computing devices,phablet computing devices, laptop computers, desktop computers, and thelike.

The system bus 410 provides an interface for the various components ofthe mobile device to inter-communicate. The system bus 410 can be of anyof several types of bus structures that can interconnect the variouscomponents (including both internal and external components). Thecomputing device 400 further includes a network communication component412 for interconnecting the computing device 400 with other networkaccessible computers, online services, and/or network entities as wellas other devices on the computer network 210. The network communicationcomponent 412 may be configured to communicate with the variouscomputers and devices over the network 108 via a wired connection, awireless connection, or both.

The computing device 400 also includes executable apps/applications 416.As those skilled in the art will appreciate, an application correspondsto a collection of executable instructions that carry out (through itsexecution on a processor) one or more tasks on a computing device, suchas computing device 400. Applications are typically, but notexclusively, executed at the direction of a user of the computingdevice. Applications combine features available on the computing devicein carrying out the various tasks (as designed by the composition of theapplication.) While the term “apps” is sometimes uses as a shorthandname for applications, in the alternative an app similarly correspondsto a collection of executable instructions for carrying out one or moretasks. However, in contrast to applications, apps typically, though notexclusively, are directed to a limited set of tasks, often focused to anarrow topic/feature. As the scope of an app is typically more limitedthan that of an application, apps typically require a smaller footprintwith regard to system resources and are often more suited for executionby computing devices of limited resources. While apps/applications 418are typically stored in memory 404, for illustration purposes only theyare called out separately from the memory 404.

The exemplary computing device 400 also includes sensors 418. Typically,sensors correspond to various hardware devices that sense particularevents relating to the computing device 400. Sensors 418 may include, byway of illustration and not limitation, accelerometers, haptic sensors,capacitive sensors, audio sensors, optic sensors, timers, temperaturesensors, power sensors (AC vs. DC sensors, voltage sensors, etc.),wireless signal sensors, geo-location sensors, magnetic sensors,altimeters, barometric sensors, and the like. Sensors may be based oncommunication information, such as internet routing data, HTTPrequest/response inspection, MAC addresses, cellular/wirelesstriangulation, and the like. As those skilled in the art willappreciate, a suitably configured computing device 400 may variouscombinations of hardware sensors 418. Moreover, these hardware sensors,as well as software sensors (as will be discussed below), are used inmonitoring the user context via an On{Event} framework.

The exemplary computing device 400 further includes a personal daemoncomponent 420 and an On{Event} framework 440. The personal daemon 420 isthe executable component which, when executed, is the personal daemon204 that provides the personal assistance to the user. As shown in FIG.4, the personal daemon 420 includes subcomponents/modules that carry outvarious functionality, include a personal assistance module 422 thatprovides the personal assistance to the associated user based on currentcontext of the user. The user sensing module 424 interfaces with theOn{Event} framework 440 to track/sense aspects of the user's currentcontent. The data analysis module 426 analyzes user-related informationto make and confirm inferences regarding the user, including inferringaddition personal information of the user. The user interface module 428provides an interface by which the user can interact with the personaldaemon 204 on the computing device 400. The personal daemon component420 maintains personal information regarding the user, as well as otheruser-related information, in a personal daemon data store 430.

Regarding the On{event} framework 440, the On{event} framework(“framework”) is an extensible event/action framework, i.e., theframework detects events that occur with regard to the one or moresensors (including sensors 418) and, in response, executes actionsassociated with the detected events on the computing device 400. It isextensible in that sensors can be added, including software sensors, andsubscribers can subscribe to sensed events.

According to aspects of the disclosed subject matter, sensors areregistered with the framework 440. By default or as part of aninitialization process, all or some of the sensors 418 may be registeredwith the framework 440. Additionally, apps and/or applications(including the apps/applications 416) can register as software sensorswith the framework 440, where a software sensor identifies the event (orevents) that it will signal and the data that may be associated with thesignaled event. Software sensors register with the framework 440 througha publisher interface 448. Sensors, including sensors 418 and softwaresensors, signal the sensed event through a sensor input interface 442.As indicated, upon receiving a sensed event, a rules executor 444executes one or more actions on the computing device 400 associated withthe sensed event, as established in the On{Event} data store 450. Appsand applications can register as subscribers to sensed/signaled eventsin the framework 440 by way of a subscription interface 446. Insubscribing to a sensed event, an app or application, as well as thepersonal daemon 204 executing on the computing device 400, indicates theevents for which the subscribing app, application, or daemon, wishes tobe notified.

Regarding the On{event} framework 440, while those skilled in the artwill appreciate that there may be a variety of alternatives to implementthe framework, in one embodiment the framework 440 is implemented as abackground service built upon the Node.js technology from Node.jsDevelopers. The Node.js technology is extensible and robust such that itcan interface with hardware sensors, such as sensors 418, as well assoftware sensors. Similarly, the personal daemon component 420 may beimplemented upon the Node.js technology. Apps and applications,including apps/applications 416, interface with Node.js processes by wayof JavaScript® code. While both the On{event} framework 440 and thepersonal daemon component 420 may be implemented using othertechnologies than Node.js, Node.js may be advantageously used as itenjoys a relatively small footprint on the host computing device, suchas computing device 400, has configurations for deployment on a numberof various operating system platforms, and JavaScript® programminglanguages enjoys broad support.

Regarding the various components of the exemplary computing device 400,those skilled in the art will appreciate that these components may beimplemented as executable software modules stored in the memory of thecomputing device, as hardware modules (including SoCs—system on a chip),or a combination of the two. Moreover, each of the various componentsmay be implemented as an independent, cooperative process or device,operating in conjunction with one or more computer systems. It should befurther appreciated, of course, that the various components describedabove in regard to the exemplary computing device 400 should be viewedas logical components for carrying out the various described functions.As those skilled in the art will readily appreciate, logical componentsand/or subsystems may or may not correspond directly, in a one-to-onemanner, to actual, discrete components. In an actual embodiment, thevarious components of each computer system may be combined together orbroke up across multiple actual components and/or implemented ascooperative processes on a computer network.

Regarding the exemplary computing device 400, it should be appreciatedthat while the personal daemon is configured to interact with theassociated user via the components of the computing device, generallyspeaking the personal daemon is independent of any particularconfiguration of computing device. Indeed, the personal daemon may beimplemented on any suitable computing device and may communicate viadisplayed messages on a display component, text messages, audio and/orvoice communications, haptic signals, and combinations thereof.

In addition to being implemented on one computing device, or acrossmultiple computing devices via sibling personal daemons, a personaldaemon may be further configured as a public mask to cooperativelyoperate in a joint computing manner with other services and/or processesin providing personal assistance to the associated user and/orperforming analysis of user activity in order to learn and/or inferadditional personal information regarding the user. However, thepersonal daemon operates in such a configuration (joint computing)according to the approval of the associated user and is restricted insharing personal information with the joint processes/services accordingto the associated user's rules for doing so. According to aspects of thedisclosed subject matter, in addition to sharing personal informationwith other third-party entities (e.g., processes and/or services)according to the associated user's explicit rules, the personal daemonmay be configured to track what personal information is disclosed tothese other entities. In tracking the disclosure of personal informationto other entities, the personal daemon is able to inform the associateduser what has been disclosed such that the user may identify limits tothe amount of personal information that may disclosed. Indeed, anassociated user may establish a limit of personal information that maybe disclosed where after the personal daemon obfuscates any additionalpersonal information that may be requested by any one entity or set ofentities.

Turning now to FIG. 5, FIG. 5 is a block diagram 500 illustratingexemplary processing stages of a personal daemon, such as personaldaemon 204, in regard to user related activity. These processing stagesrepresent an enrichment cycle for the personal daemon, i.e., theprocesses of learning/inferring information regarding the associateduser and then applying the information the benefit of the associateduser. To begin the discussion, we can assume that the personal daemonreceives notice of a subscribed event 501. By way of example and notlimitation, an event may indicate that the user's computing device isreceiving an incoming telephone call, or that the associated user haschanged his/her location (as sensed by the geo-location sensor on thecomputing device).

Upon receiving notice of the subscribed event 501 and according toinformation associated with the event, the personal daemon determineswhether to provide personal assistance to the associated user in regardto the event, as indicated by circle 502. This determination is based onthe information regarding the current context of the associated user,including personal information of the user, as well as rules previouslyestablished for the particular combination of events and context. Forexample, assume that the associated user is currently at work and thepersonal daemon knows this according to events received regarding thegeo-location of the user's smart-phone/computing device according torules and personal information in the personal daemon data store 432.Additionally, as a rule (which rule the personal daemon has eitherlearned through inference, explicit direction from the user, or acombination of the two), the user typically does not take phone calls onhis or her smart-phone while at work. However, yet another ruleestablished with the personal daemon (again by inference, explicitinstruction, or both) that the associated user will answer his or hersmart-phone if it is during lunch or it is from specific individuals(such as a spouse.) Thus, at circle 502, when the subscribed event 501is in regard to an incoming telephone call, the personal daemon receivesthe event and provides personal assistance to the user according to itsrules regarding the user and the user's current context. Thus, if theinformation associated with the event indicates that the incomingtelephone call is from an acquaintance, the personal daemon 204according to its internal rules may immediately direct the incomingtelephone call to an answering service. Alternative, if the informationassociated with the event 501 indicates that the incoming telephone callis from a spouse, the then personal daemon 204 can provide personalassistance to the associated user by permitting the incoming call toring on the user's smart phone.

In addition providing immediate personal assistance, as indicated incircle 504, another part of the personal daemon 204 recordsinformation/data in regard the received event 501 in a user informationdata store 503. According to aspects of the disclosed subject matter,the personal daemon 204 records and logs events, contexts, and dataassociated with the user and the user's activities. This information isthen used later in the analysis of user information, as indicated bycircle 506, in learning and making inferences regarding additionalpersonal information regarding the user, and in also learning rules forproviding personal assistance to the user in regard to various eventsand contexts. This learning activity is described below in regard toroutine 700 of FIG. 7. Of course, event information is not the only datathat is stored in the user information data store 503. The personaldaemon 204, due to its trusted position, also monitors user activitywith regard to other apps, applications, online activities and the liketo gain additional personal information. Submitted search queries,browsing history, social network site interactions, retrieved newsarticles, and the like are recorded in the user information data storesuch that the analysis activity (as denoted by circle 506) can refineand augment the personal information the persona daemon maintainsregarding the associated user. While the user information data store 503is indicated as being a separate entity from the personal daemon datastore 432, this is for illustration purposes and should not be construedas limiting upon the disclosed subject matter. According to variousembodiments, the user information data store 503 is a part of thepersonal daemon data store 432.

In analysis activity, as indicated by circle 506, the personal daemon204 analyzes the information, as found in the user information datastore 503, regarding the associated user, as well as and in light of thepersonal information know about the associated user in the personaldaemon data store 432. The analysis activity uses neural networks,machine learning models, pattern recognition, and the like to inferinformation regarding the associated user. The analysis activity mayfurther validate its inferences with the associated user by way of aconfirmation dialog, though not necessarily in synchronicity uponderiving various inferences. The inferences may include static personalinformation (e.g., where the associated user works, theusername/password of the user on a social networking site, etc.) ordynamic personal information (e.g., rules for responding to particularevents, etc.) Based on the results of the analysis, the personalinformation regarding the associated user is refined and/or augmented inthe personal daemon data store 432.

It should be appreciated that the analysis activity, as indicated bycircle 506, will often include a confirmation dialog with the associateduser. Typically, inferences are associated with some level ofconfidence. Except for the occasions in which the analysis activityproduces an inference with near certainty of confidence, the personaldaemon will often need to interact with the user in a confirmation typedialog, where inferences of personal information are presented to theuser for confirmation or rejection. With regard to the example ofdetermining the location where the associated user works, upon the firstinference the personal daemon may engage the associated user with adialog such as “Is this your work location?” The associated user mayconfirm or reject the inference. For example, the associated user mayindicate that inferred location it is not a work location, but locationof a school that the associated user attends. Through confirmationdialogs, as well as explicit review of inferred personal information andrules, the user exercises complete control over his/her personalinformation.

As part of or as a result of learning/inferring addition personalinformation regarding the user, and as part of providing personalassistance to the associated user (circle 502), the personal daemon maytake proactive steps such as downloading data that may be relevant tothe user. For example, as part of learning the location where theassociated user works and based on personal information about the userthat he or she likes a particular cuisine, the personal daemon mayproactively download restaurant information surrounding the user's worklocation for future reference. Based on personal information regardingthe associated user's work location and commuting habits, the personaldaemon may associate a rule with a timer event to check the trafficsituation for the commute and provide recommendations to the user whenpoor commuting conditions are present.

A distinct advantage that a personal daemon 204 has over a monolithiconline service is that the personal daemon needs only maintain datarelevant to the associated user. Maps, restaurants, calendars of events,etc. that are relevant to the associated user, as well as recording userrelated information such as search queries, browsing history, socialnetworking profiles, etc., requires substantially less storage capacitythan capturing and storing all information to serve a large number ofusers. Indeed, while the amount of information that may be of relevanceto the user is not insignificant, in the context of the capacity ofcurrent computing devices, maintaining such information on a computingdevice is manageable. Additionally, as the personal daemon is situatedon the “edge of the cloud,” to the extent that information is notcurrently available, is temporal, or is beyond the capacity of its hostcomputing device, the personal daemon 204 can access such informationonline. For example, in the above-mentioned example of obtaining trafficinformation regarding the associated user's commute, the personal daemonmay be configured to access the traffic information from an externalsource rather than retrieving and storing the information in the userinformation data store 503.

As indicated above, the personal daemon 204 does not share personalinformation regarding the associated user with other entities except asexplicitly directed by the user. For example, the user may subscribe toa social networking site where access to the site is gained by supplyinga password. Further, the personal daemon may establish rules forproviding notice to the associated user whenever content is posted onthe social networking site by a particular user. While the personaldaemon may associate a timer rule to periodically check on the socialnetworking site for such posts, to access the information the personaldaemon would need to provide the user's password and account informationto the site to gain access. This activity, of course, is divulging theuser's personal information. However, based on rules established by thepersonal daemon and according to explicit or inferred authorization bythe associated user, the personal daemon may be authorized to divulgethe personal information in providing personal assistance to the user.

Of course, in the preceding example, the networking site may capturecertain personal information regarding the user, e.g., user preferences,demographic information, geographic information, etc. Moreover, thenetworking site may also be vendor-funded such that advertisements arepresented to the user when accessing the site. This, then, illustratesthat while the personal daemon 204 does not share personal informationregarding the associated user, the associated user is not restricted outof accessing and interacting with sites that may be vendor-fundedthrough the disclosure of personal information, including the monolithiconline sites discussed above.

FIG. 6 is a flow diagram illustrating an exemplary routine 600, asimplemented by a personal daemon 204, in providing personal assistanceto the associated user in response to an event related to the user.Beginning at block 602, the personal daemon 204 receives notice of asubscribed event 501. As suggested above, the subscribed event maycorrespond to any number of events sensed by both hardware and softwaresensors. At block 604, the personal daemon consults the personal daemondata store 432 for personal assistance rules corresponding to thereceived event. At decision block 606 a determination is made as towhether there are any rules associated with the received event. If thereare no rules associated with the received event 501, the routine 600terminates. Alternatively, if there are rules associated with thereceived event 501, the routine 600 proceeds to block 608.

At block 608, the personal daemon identifies personal assistance actionsto be taken in regard to the received event. At decision block 610, ifthere are no actions to be taken, the routine 600 terminates. However,if there are actions to be taken, at block 612, the actions areconfigured according to current constraints. Generally speaking,configured the action according to current constraints comprisesadapting the execution of the action according to the current context ofthe associated user. Personalization rules for adapting an action may bedetermined for the current context from the personal daemon data store432. For example, if the received event is in regard to trafficcongestion on the associated user's typical route home, the action maybe to notify the user of the traffic congestion and suggest analternative. Further still, the current context of the user may be thathe/she is currently in a meeting and he/she should not be notified ofnon-emergency items during meetings. Hence, configuring the actionaccording to current constraints would mean delaying the delivery of thesuggested alternative route until the meeting is over. At block 614, theconfigured actions are executed in according to the various constraints,if any, from block 612. Thereafter, the routine 600 terminates.

As suggested above, one of the advantages of the trusted nature of apersonal daemon is that it can use its access to the associated user'spersonal information to learn additional personal information throughanalysis activity (see circle 506 of FIG. 5), including both data andrules of behavior, in order to more fully become an extension of theuser. FIG. 7 is a flow diagram illustrating an exemplary routine 700 forconducting analysis of user activity to learn and adapt to additionalpersonal information of the associated user. Beginning at block 702, theuser's actions are analyzed. This analysis is made on current andhistorical information and actions of the associated user, currentlyestablished rules, as well as the user's personal information (asmaintained by the personal daemon in the personal daemon data store430).

At block 704, one or more inferences are generated according to theanalysis activity of block 702. These inferences generate additionaland/or refined personal information of the associated user, as well asadditional and/or refined rules for providing personal assistance to theuser. As used herein, generating inferences regarding the associateduser corresponds to inferring information about the user, rules forproviding personal assistance to the user and the like. As indicatedabove, the generated inferences are made upon the various events andassociated contexts regarding the user, both current and past, theuser's interaction and behaviors with regard to the events, personalinformation of the user, as well as previously inferred rules forproviding personal assistance to the user. As those skilled in the artwill appreciate, inference can be employed to identify a specificcontext or action, or can generate a probability distribution overcandidate states. An inference can be probabilistic, i.e., the inferencemay be associated with a probability or likelihood of occurrence withregard to a given state of interest based on a consideration of data andevents. Inference techniques can be employed to generate higher-levelevents, e.g., rules for providing personal assistance from a set ofrecorded events and/or know or assumed data. Thus, inferences can resultin the construction of new information or actions/rules from a set ofobserved events and/or stored event data. Advantageously, the inferencesmay be generated from events and data are not necessarily correlated inclose temporal proximity, and/or from events and data that come from oneor more sources.

Assuming that the generated inferences were determined as aprobabilistic inference, at decision block 706 a determination is madeas to whether or not any of the generated inferences are sufficiently“strong” that they do not need to be confirmed by the associated user.In one exemplary embodiment (for illustration and not limitation), aninference is sufficiently strong if the likelihood of occurrence isgreater than a predetermined threshold value, e.g., a 95% estimatedlikelihood of occurring given the same (or substantially similar)events, context, and data. In an alternative embodiment, all inferencesregarding the user's personal information or rules for providingpersonal assistance to the user that are generated in the analysisactivity are confirmed with the user before implementation.Alternatively still, implementation and use of the inferred personalinformation and rules may conditionally occur, pending furtherconfirmation, when the probabilistic likelihood exceeds a predeterminedthreshold, e.g., a 75% estimated likelihood of occurrence.

In the event that one or more generated inferences are not sufficientlystrong, or that all inferences should be confirmed, at block 708 theinferences are confirmed with the user. Confirming inferences typicallyinvolves user interaction to confirm inferred personal data and/or rulesfor providing personal assistance. In confirming the generatedinferences, the bases for the inference may be presented to the user,i.e., the event, personal information and context upon which theinferences was drawn. As will all of the personal information (includingboth data and rules for providing personal assistance) maintained by thepersonal daemon, the associated user has full control over this datasuch that he/she may delete, modify, confirm any and all parts of suchpersonal information. This is important as an inferred rule may involvedisclosing personal information regarding the user to another service orentity, in which case it is important for the associated user to be ableto exercise control over such data (including stopping the disseminationof the data, permitting the disclosure in the particular context, andthe like.)

Confirming inferences may involve a dialog between the personal daemonand the associated user (on the user's mobile device) in which thepersonal daemon iterates through the unconfirmed inferences, iterativelypresenting each unconfirmed inference (and, potentially, the bases forits generation) and requests feedback from the user, includingacceptance, modification, delaying a decision, or rejection. As analternative to this dialog approach, or in addition to this iterativedialog approach, when conditions in which an unconfirmed inference maybe used in providing personal assistance to the user, a dialog (i.e., apresentation to the user on the mobile device which may involvedisplaying information on a display screen, an audio presentation,signaling the user in some fashion, etc.) specifically directed to theunconfirmed inference at current issue may be presented to the user. Forexample, assuming that the personal daemon recognizes that theassociated user is preparing to leave work for his/her home residence, anotice may be generated to the user from the personal daemon suggestingthat the daemon check on the traffic status of the user's typical routehome.

At block 710, after having confirmed the generated inferences or, thegenerated inferences are of sufficient strength that the user does notwish to confirm them, the associated user's personal information,including both data and rules for providing personal assistance, areupdated. Thereafter, routine 700 terminates.

As those skilled in the art will readily appreciate, through thisprocess of analysis of user activity and data, generation of inferencesregarding the user, and confirmation of inferences, the personal daemoncontinually adapts itself to provide ever improving personal assistance.Continued application of these steps (analysis, inference, confirmation,and—of course—application of the information via personal assistance)refines the personal daemon to the point that it literally becomes anextension of one's self, reflecting the preferences and habits of theassociated user.

Regarding routines 600 and 700, as well as other processes describeabove, while these routines/processes are expressed in regard todiscrete steps, these steps should be viewed as being logical in natureand may or may not correspond to any actual and/or discrete steps of aparticular implementation. Nor should the order in which these steps arepresented in the various routines be construed as the only order inwhich the steps may be carried out. Moreover, while these routinesinclude various novel features of the disclosed subject matter, othersteps (not listed) may also be carried out in the execution of theroutines. Further, those skilled in the art will appreciate that logicalsteps of these routines may be combined together or be comprised ofmultiple steps. Steps of routines 600 and 700 may be carried out inparallel or in series. Often, but not exclusively, the functionality ofthe various routines is embodied in software (e.g., applications, systemservices, libraries, and the like) that is executed on computing devicesas described in regard to FIG. 4. In various embodiments, all or some ofthe various routines may also be embodied in hardware modules, includingbut not limited to system on chips, specially designed processors and orlogic circuits, and the like on a computer system.

These routines/processes are typically implemented in executable codecomprising routines, functions, looping structures, selectors such asif-then and if-then-else statements, assignments, arithmeticcomputations, and the like. The exact implementation of each of theroutines is based on various implementation configurations anddecisions, including programming languages, compilers, targetprocessors, operating environments, and the link. Those skilled in theart will readily appreciate that the logical steps identified in theseroutines may be implemented in any number of manners and, thus, thelogical descriptions set forth above are sufficiently enabling toachieve similar results.

While many novel aspects of the disclosed subject matter are expressedin routines embodied in applications (also referred to as computerprograms), apps (small, generally single or narrow purposed,applications), and/or methods, these aspects may also be embodied ascomputer-executable instructions stored by computer-readable media, alsoreferred to as computer-readable storage media. As those skilled in theart will recognize, computer-readable media can host computer-executableinstructions for later retrieval and execution. When thecomputer-executable instructions store stored on the computer-readablestorage devices are executed, they carry out various steps, methodsand/or functionality, including those steps, methods, and routinesdescribed above in regard the various routines. Examples ofcomputer-readable media include, but are not limited to: optical storagemedia such as Blu-ray discs, digital video discs (DVDs), compact discs(CDs), optical disc cartridges, and the like; magnetic storage mediaincluding hard disk drives, floppy disks, magnetic tape, and the like;memory storage devices such as random access memory (RAM), read-onlymemory (ROM), memory cards, thumb drives, and the like; cloud storage(i.e., an online storage service); and the like. For purposes of thisdisclosure, however, computer-readable media expressly excludes carrierwaves and propagated signals.

According to aspects of the disclosed subject matter, numerous technicalbenefits are realized through the use of a personal daemon overalternative solutions. These technical benefits include, by way ofillustration, improved latency in providing personal assistance as thepersonal daemon resides and executes on the “edge of the cloud” therebyeliminating the communication time with a remote service;correspondingly, local execution minimizes the bandwidth usage over thenetwork; elements of the personal daemon (such as determining personalinformation and inferences by analysis) may be executed during non-peakprocessing times, i.e., when the user's demands on the computing deviceare low; enables personal assistance even when the computing device doesnot have network connectivity; makes use of preemptive caching ofinformation based on predicted needs, which caching may be completed attimes of network connectivity; low cost of implementation as thepersonal daemon operates within bounds of the associated user's owncomputing device; provides substantially improved data security aspersonal information is not shared with others without explicit rules todo so; and provides proactive augmentation of personal data and personalassistance without third party surveillance.

While various novel aspects of the disclosed subject matter have beendescribed, it should be appreciated that these aspects are exemplary andshould not be construed as limiting. Variations and alterations to thevarious aspects may be made without departing from the scope of thedisclosed subject matter.

What is claimed:
 1. A mobile computing device configured to providepersonal assistance to an associated user, the computing devicecomprising a processor and a memory, wherein the processor executesinstructions to provide personal assistant to the associated user, theadditional components comprising: a personal daemon configured to:operate in the background of the mobile computing device; maintain adata store of personal information regarding the associated user;monitor actions of the associated user made in conjunction with themobile computing device; analyze the actions of the associated user todetermine additional personal information of the associated user;receives notice of events from the plurality of sensors; and executes apersonal assistance action on behalf of the associated user in responseto a received notice of an event and according to the personalinformation maintained by the personal daemon; wherein the personaldaemon is further configured to not share the personal information ofthe associated user with any other entity other than the associated userexcept under conditions of rules established by the associated user. 2.The computing device of claim 1 further comprising: a plurality ofsensors each configured to sense one or more conditions; and anon{event} framework configured to: monitor the plurality of sensors;identify a set of subscribing processes of a sensed condition by one ofthe plurality of sensors; and generates a notice of event to thesubscribing processes upon the sensing of the sensed condition; whereinthe personal daemon subscribes to a plurality of sensed conditions withthe on{event} framework.
 3. The computing device of claim 2, wherein atleast some of the plurality of sensors are configured to detectconditions relating to aspects of the current context of the associateduser.
 4. The computing device of claim 3, wherein the on{event}framework is configured to monitor the plurality of sensors comprisingboth hardware and software sensors.
 5. The computing device of claim 4,wherein the on{event} framework is configured to permit software apps orapplications to publish to the framework as a software sensor.
 6. Thecomputing device of claim 2, wherein the personal daemon providespersonal assistance to the associated user based on the current contextof the associated user and according to the personal information of theassociated user.
 7. The computing device of claim 6, wherein thepersonal information of the associated user comprises rules forproviding personal assistance according to the current context of theassociated user.
 8. The computing device of claim 7, wherein providingpersonal assistance to the associated user comprises executing one ormore actions on the computing device for the benefit of the associateduser.
 9. The computing device of claim 7, wherein determining additionalpersonal information of the associated user comprises inferring rulesfor providing assistance to the associated user according to the currentcontext of the associated user.
 10. The computing device of claim 1,wherein the personal daemon is configured to analyze the actions of theassociated user to determine additional personal information of theassociated user at periods of low processor use of the computing deviceby the associated user.
 11. The computing device of claim 1, wherein thepersonal daemon is configured to provide the personal assistance to theassociated user irrespective of network connectivity.
 12. The computingdevice of claim 1, wherein the personal daemon is further configured tonot share the personal information of the associated user with any otherentity other than the associate user or with sibling personal daemonsexecuting on other computing devices of the associated user, or exceptunder the conditions of rules established by the associated user. 13.The computing device of claim 1, wherein providing personal assistanceto the associated user comprises any of: providing a recommendation tothe associated user that the associated user take a particular action onthe computing device; confirming an inference of personal informationfrom analysis of the associated user's activities; confirmingauthorization for the personal daemon take an action on behalf of theassociated user; providing a notification to the associated userregarding one or more events sensed by one of the plurality of sensors;providing alternatives to current user activities; and executing apredetermined action on behalf of the user on the computing device. 14.The computing device of claim 13, wherein the predetermined action isdetermined according to an inference derived from analyzing the actionsof the associated user to determine additional personal information ofthe associated user.
 15. A computing device-implemented method forproviding personal assistance to a user, the method comprising each ofthe following as implemented by a personal daemon process running in thebackground on a mobile computing device: receiving a notice of asubscribed event relating to the user; consulting a set of personalassistance rules in a store of personal information of the user toidentify one or more actions to be taken on behalf of the user in regardto receiving the subscribed event; executing, without user input, theidentified actions on the computing device on behalf of the user. 16.The computing device-implemented method of claim 17 further comprising:recording the user's actions with regard to using the computing device;analyzing the user's actions; based on the analysis, generating one ormore personal assistance rules identifying one or more actions to betaken on behalf of the user in regard to receiving the subscribed event;and storing the generated one or more personal assistance rules in astore of personal information of the user.
 17. The computingdevice-implemented method of claim 16, wherein generating the one ormore personal assistance rules is based on an inference, and wherein themethod further comprises presenting a confirmation dialog to the user toconfirm the inference.
 18. The computing device-implemented method ofclaim 17, wherein providing personal assistance to the associated usercomprises any of: providing a recommendation to the user that the usertake a particular action on the mobile computing device; confirmingauthorization for the personal daemon take an action on behalf of theuser; and executing a predetermined action on behalf of the user on themobile computing device.
 19. A computer-readable medium bearingcomputer-executable instructions which, when executed on a mobilecomputing device having at least a processor and a memory, carry out amethod for providing personal assistance to a user, the methodcomprising: receiving a notice of a subscribed event relating to theuser; consulting a set of personal assistance rules in a store ofpersonal information of the user to identify one or more actions to betaken on behalf of the user in regard to receiving the subscribed event;executing, without user input, the identified actions on the computingdevice on behalf of the user.
 20. The computer-readable medium of claim19, wherein the method further comprises: recording the user's actionswith regard to using the computing device; analyzing the user's actions;based on the analysis, generating one or more personal assistance rulesidentifying one or more actions to be taken on behalf of the user inregard to receiving the subscribed event; and storing the generated oneor more personal assistance rules in a store of personal information ofthe user.